260,353 research outputs found
Distributed Representations of Signed Networks
Recent successes in word embedding and document embedding have motivated
researchers to explore similar representations for networks and to use such
representations for tasks such as edge prediction, node label prediction, and
community detection. Such network embedding methods are largely focused on
finding distributed representations for unsigned networks and are unable to
discover embeddings that respect polarities inherent in edges. We propose
SIGNet, a fast scalable embedding method suitable for signed networks. Our
proposed objective function aims to carefully model the social structure
implicit in signed networks by reinforcing the principles of social balance
theory. Our method builds upon the traditional word2vec family of embedding
approaches and adds a new targeted node sampling strategy to maintain
structural balance in higher-order neighborhoods. We demonstrate the
superiority of SIGNet over state-of-the-art methods proposed for both signed
and unsigned networks on several real world datasets from different domains. In
particular, SIGNet offers an approach to generate a richer vocabulary of
features of signed networks to support representation and reasoning.Comment: Published in PAKDD 201
Phantom cascades: The effect of hidden nodes on information diffusion
Research on information diffusion generally assumes complete knowledge of the
underlying network. However, in the presence of factors such as increasing
privacy awareness, restrictions on application programming interfaces (APIs)
and sampling strategies, this assumption rarely holds in the real world which
in turn leads to an underestimation of the size of information cascades. In
this work we study the effect of hidden network structure on information
diffusion processes. We characterise information cascades through activation
paths traversing visible and hidden parts of the network. We quantify diffusion
estimation error while varying the amount of hidden structure in five empirical
and synthetic network datasets and demonstrate the effect of topological
properties on this error. Finally, we suggest practical recommendations for
practitioners and propose a model to predict the cascade size with minimal
information regarding the underlying network.Comment: Preprint submitted to Elsevier Computer Communication
Community detection for networks with unipartite and bipartite structure
Finding community structures in networks is important in network science,
technology, and applications. To date, most algorithms that aim to find
community structures only focus either on unipartite or bipartite networks. A
unipartite network consists of one set of nodes and a bipartite network
consists of two nonoverlapping sets of nodes with only links joining the nodes
in different sets. However, a third type of network exists, defined here as the
mixture network. Just like a bipartite network, a mixture network also consists
of two sets of nodes, but some nodes may simultaneously belong to two sets,
which breaks the nonoverlapping restriction of a bipartite network. The mixture
network can be considered as a general case, with unipartite and bipartite
networks viewed as its limiting cases. A mixture network can represent not only
all the unipartite and bipartite networks, but also a wide range of real-world
networks that cannot be properly represented as either unipartite or bipartite
networks in fields such as biology and social science. Based on this
observation, we first propose a probabilistic model that can find modules in
unipartite, bipartite, and mixture networks in a unified framework based on the
link community model for a unipartite undirected network [B Ball et al (2011
Phys. Rev. E 84 036103)]. We test our algorithm on synthetic networks (both
overlapping and nonoverlapping communities) and apply it to two real-world
networks: a southern women bipartite network and a human transcriptional
regulatory mixture network. The results suggest that our model performs well
for all three types of networks, is competitive with other algorithms for
unipartite or bipartite networks, and is applicable to real-world networks.Comment: 27 pages, 8 figures.
(http://iopscience.iop.org/1367-2630/16/9/093001
Data DNA: The Next Generation of Statistical Metadata
Describes the components of a complete statistical metadata system and suggests ways to create and structure metadata for better access and understanding of data sets by diverse users
Arboreal twig-nesting ants form dominance hierarchies over nesting resources.
Interspecific dominance hierarchies have been widely reported across animal systems. High-ranking species are expected to monopolize more resources than low-ranking species via resource monopolization. In some ant species, dominance hierarchies have been used to explain species coexistence and community structure. However, it remains unclear whether or in what contexts dominance hierarchies occur in tropical ant communities. This study seeks to examine whether arboreal twig-nesting ants competing for nesting resources in a Mexican coffee agricultural ecosystem are arranged in a linear dominance hierarchy. We described the dominance relationships among 10 species of ants and measured the uncertainty and steepness of the inferred dominance hierarchy. We also assessed the orderliness of the hierarchy by considering species interactions at the network level. Based on the randomized Elo-rating method, we found that the twig-nesting ant species Myrmelachista mexicana ranked highest in the ranking, while Pseudomyrmex ejectus was ranked as the lowest in the hierarchy. Our results show that the hierarchy was intermediate in its steepness, suggesting that the probability of higher ranked species winning contests against lower ranked species was fairly high. Motif analysis and significant excess of triads further revealed that the species networks were largely transitive. This study highlights that some tropical arboreal ant communities organize into dominance hierarchies
Social Bootstrapping: How Pinterest and Last.fm Social Communities Benefit by Borrowing Links from Facebook
How does one develop a new online community that is highly engaging to each
user and promotes social interaction? A number of websites offer friend-finding
features that help users bootstrap social networks on the website by copying
links from an established network like Facebook or Twitter. This paper
quantifies the extent to which such social bootstrapping is effective in
enhancing a social experience of the website. First, we develop a stylised
analytical model that suggests that copying tends to produce a giant connected
component (i.e., a connected community) quickly and preserves properties such
as reciprocity and clustering, up to a linear multiplicative factor. Second, we
use data from two websites, Pinterest and Last.fm, to empirically compare the
subgraph of links copied from Facebook to links created natively. We find that
the copied subgraph has a giant component, higher reciprocity and clustering,
and confirm that the copied connections see higher social interactions.
However, the need for copying diminishes as users become more active and
influential. Such users tend to create links natively on the website, to users
who are more similar to them than their Facebook friends. Our findings give new
insights into understanding how bootstrapping from established social networks
can help engage new users by enhancing social interactivity.Comment: Proc. 23rd International World Wide Web Conference (WWW), 201
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